Summary of Perceive, Reflect, and Plan: Designing Llm Agent For Goal-directed City Navigation Without Instructions, by Qingbin Zeng et al.
Perceive, Reflect, and Plan: Designing LLM Agent for Goal-Directed City Navigation without Instructions
by Qingbin Zeng, Qinglong Yang, Shunan Dong, Heming Du, Liang Zheng, Fengli Xu, Yong Li
First submitted to arxiv on: 8 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles a challenging problem in city navigation where an AI agent must navigate to a goal location based on language descriptions of landmarks and scene observations without explicit instructions. The task requires the agent to establish its own position and create a spatial representation of the urban environment, which can be complex with invisible landmarks. Large language models (LLMs) are tempting baselines, but they often make poor decisions due to a lack of reasoning abilities. To address this issue, the paper introduces a novel workflow featuring perception, reflection, and planning. Specifically, it shows that LLaVA-7B can be fine-tuned for accurate landmark direction and distance perception, while reflection uses memory mechanisms to store past experiences and inform decision-making. Planning then produces long-term plans that avoid short-sighted decisions in long-range navigation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers try to solve a tricky problem where an AI agent has to find its way to a specific location based on descriptions of landmarks and what it sees around it. The goal is to create a smart system that can navigate through cities without being told exactly how to get there. They test different approaches using large language models (LLMs) but find that these systems don’t work well because they lack common sense and problem-solving skills. To fix this, the researchers develop a new way of working that involves perceiving what’s around, reflecting on past experiences, and planning ahead. This helps the AI agent make better decisions and avoid getting stuck in loops. |
